摘要
目的:借助网络药理学手段分析仙茅苷治疗骨质疏松症(OP)的潜在靶点及相关作用机制。方法:从基因表达数据库(GEO)下载GSE85871的微阵列数据集,预测仙茅苷的治疗靶点,同时从疾病相关基因公共数据库检索已知OP的致病基因,利用STRING及Cytoscape软件构建了映射关键靶点的蛋白质-蛋白质相互作用(PPI)网络,利用DAVID进行KEGG通路和GO富集分析以阐明其可能的作用机制。结果:本研究利用R语言筛选出107个仙茅苷潜在治疗靶点,通过疾病相关基因数据库检索出691个OP致病靶点,借助Cytoscape构建PPI网络,筛选出仙茅苷治疗OP的26个关键靶点。KEGG通路和GO富集分析结果显示,仙茅苷治疗OP的关键靶点主要富集的生物学进程为肾脏发育、单核细胞分化和上皮细胞增殖;信号通路主要富集在MAPK信号通路、TGF-β信号通路等。结论:基于网络药理学方法分析,仙茅苷可能通过MAPK信号通路和TGF-β信号通路治疗OP。
Objective:To analyze the potential targets and mechanism in the treatment of osteoporosis(OP)by curculigoside based on network pharmacology.Methods:The microarray data set of GSE85871 was downloaded from the gene expression database(GEO),and the targets in the treatment of OP by curculigoside was predicted.At the same time,the known pathog-enic genes of OP were retrieved from the public database of disease-related genes.The protein-protein interaction(PPI)network mapping the key targets was constructed by using STRING and Cytoscape software,and the KEGG pathway and GO enrichment analysis were carried out by using DAVID to clarify the mechanism.Results:In this study,107 potential targets in the treatment of OP by curculigoside were screened out by R language,691 pathogenic targets of OP were retrieved from the disease-related gene database,and 26 key targets in the treatment of OP by curculigoside were screened out by constructing PPI network with the help of Cytoscape.The results of KEGG pathway and GO enrichment analysis showed that the key targets in the treatment of OP by curculigoside were mainly in the biological process of kidney development,monocyte differentiation and epithelial cell proliferation.The signal pathway was mainly enriched in MAPK signal pathway,TGF-β signal pathway and so on.Conclusion:Based on the analysis by network pharmacology,curculigoside may treat OP through MAPK signal pathway and TGF-β signal pathway.
作者
陈儒
王卫国
CHEN Ru;WANG Weiguo(Shandong University of Traditional Chinese Medicine,Jinan 250355,China)
出处
《山东中医杂志》
2020年第8期861-867,共7页
Shandong Journal of Traditional Chinese Medicine
基金
山东省保健科技协会科学技术课题(编号:SDBJKT20170024)。
